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Glama

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Supply-chain network design via simulation, optimization, and greenfield analysis.

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Healthy
Last Tested
Transport
Streamable HTTP
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Glama
MCP server

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Tool DescriptionsA

Average 4.5/5 across 11 of 11 tools scored.

Server CoherenceA
Disambiguation5/5

Tools are cleanly separated into three domains (greenfield, optimization, simulation) with distinct purposes. Each domain has list, describe, get, and explain variants that are clearly differentiated by their prefixes and descriptions.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using underscore_case. Verbs like list_, describe_, get_, explain_, run_ are used uniformly across domains, making the naming predictable.

Tool Count5/5

11 tools is well-scoped for a supply chain modeling server covering three main areas. Each area has a minimal but complete set of operations (list, describe, get, explain) without superfluous tools.

Completeness5/5

The tool surface covers the full lifecycle for sample demos: listing available demos/models, getting details, retrieving precomputed results, and explaining underlying concepts. No obvious gaps in functionality for the server's stated purpose.

Available Tools

11 tools
describe_greenfield_demo
Read-onlyIdempotent
Inspect

Full detail on one greenfield demo — region, customer count, available dc_count values, and the score-curve elbow finding. Use this before get_greenfield_result to know what dc_count values are precomputed.

ParametersJSON Schema
NameRequiredDescriptionDefault
demo_idYesWhich greenfield demo to describe

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
describe_opt_demo
Read-onlyIdempotent
Inspect

Full detail on one optimization demo — controls, available scenario keys, sites, fixed parameters, citations, and the key finding the demo illustrates. Use this before get_opt_result to know what scenario_key values are accepted.

ParametersJSON Schema
NameRequiredDescriptionDefault
demo_idYesWhich optimization demo to describe

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
explain_greenfield
Read-onlyIdempotent
Inspect

Reference text on greenfield analysis — clean-slate facility-location math. Covers the weighted center-of-gravity (Weber) formulation, Weiszfeld's iterative algorithm, Lloyd's-style alternating location-allocation for N facilities, service constraints (% demand vs % customers within a distance band), and the inverse problem of solving for minimum N. Also covers when to use greenfield vs facility selection (the open/close MIP). Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does greenfield analysis work' or 'where would I put my DCs' question. ChiAha's GreenfieldAnalysis engine powers the US Greenfield Design demo on the sandbox.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
explain_optimization
Read-onlyIdempotent
Inspect

Reference text on supply-chain network optimization — mixed-integer programming (MIP), the structure of decision variables and constraints, the objective function for landed-cost minimization, and the common problem classes (facility selection, sourcing, flow constraints, multi-period, BOM/production, multi-objective). Also covers when to reach for optimization vs simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does network optimization work' question. ChiAha's AMOS optimizer (open-source, Odin, GLOP/CBC via OR-Tools) powers the Tariff and Coffee Co-pack demos on the sandbox.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
get_greenfield_result
Read-onlyIdempotent
Inspect

Get the precomputed result for one DC count of a greenfield demo. Returns sited DCs (lat/lon + city/state, nearest-city snapped), customer-to-DC assignments, and the score for that DC count. ANTI-FABRICATION: every result is verbatim engine output from greenfield-cli — quote them in your reply, do not round or fabricate cities.

ParametersJSON Schema
NameRequiredDescriptionDefault
demo_idYesWhich greenfield demo
dc_countYesNumber of DCs to site (2-8)

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
get_opt_result
Read-onlyIdempotent
Inspect

Get the precomputed result for one scenario of an optimization demo. Returns the verbatim engine output JSON (AMOS for tariff/coffee, SSO output for sso-basic) including the optimal sourcing/production/transport decisions, costs, and any open/close facility variables. ANTI-FABRICATION: every numeric result is verbatim from the optimization engine that ran offline — quote them in your reply, do not round or recompute. Call describe_opt_demo first to learn valid scenario_key formats for each demo.

ParametersJSON Schema
NameRequiredDescriptionDefault
demo_idYesWhich optimization demo
scenario_keyNoScenario key within the demo. Format varies per demo — call describe_opt_demo for the exact valid keys before guessing. Tariff uses 'APAC=<N>' where N is one of 0, 7.5, 25, 50, 100. Coffee uses '<configKey>|DSL=<N>' where configKey is T/TA/TS/TAS and N is 20-70 in steps of 5 (10c units of $/gal). sso-basic is single-scenario; scenario_key is ignored.

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
get_sc_theory
Read-onlyIdempotent
Inspect

Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
list_greenfield_demos
Read-onlyIdempotent
Inspect

List the bundled SCModeling greenfield demos. Returns id + label + one-line summary. Currently one demo (US, 189 customer points). Use this before describe_greenfield_demo or get_greenfield_result.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
list_models
Read-onlyIdempotent
Inspect

List the bundled SCModeling sample supply-chain models. Returns a catalog with each model's id and a short description. Use this before run_simulation to know which model_id values are valid.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
list_opt_demos
Read-onlyIdempotent
Inspect

List the bundled SCModeling optimization demos. Returns id + label + one-line summary for each (Tariff, Coffee Co-pack, SSO Basic). Use this before describe_opt_demo or get_opt_result to know which demo_id values are valid. All demos are precomputed sample-only fixtures — for optimization on real client data, the SCModeling desktop tool is the product.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentNoMCP content blocks — single text block with the response body
run_simulation
Read-only
Inspect

Run a supply-chain simulation on a bundled SCModeling sample model (sdi-db). Returns metrics, inventory time-series, orders, shipments, routing and BOM. ANTI-FABRICATION: the returned numbers come from a real discrete-event simulation run on the sc-sim engine. Quote them VERBATIM in your reply. Do not round, estimate, average, or compute derived figures from training-data recall. If the user asks a follow-up about the same model, re-call this tool rather than recalling numbers from earlier in the conversation.

ParametersJSON Schema
NameRequiredDescriptionDefault
model_idYesWhich sample model to simulate

Output Schema

ParametersJSON Schema
NameRequiredDescription
detailsNoFull run detail: config, locations, materials, routing, demands, orders, shipments, inventory_timeseries
metricsNoTop-line scalar KPIs (orders, shipments, simulation_days)
metadataNoModel name, version, timestamp

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